CN114740497B - UKF multisource fusion detection-based unmanned aerial vehicle deception method - Google Patents
UKF multisource fusion detection-based unmanned aerial vehicle deception method Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/015—Arrangements for jamming, spoofing or other methods of denial of service of such systems
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
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- G01S19/21—Interference related issues ; Issues related to cross-correlation, spoofing or other methods of denial of service
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Abstract
The invention provides an unmanned aerial vehicle deception method based on UKF multisource fusion detection, which adopts a radar/photoelectric composite guidance information fusion system to carry out fusion detection on targets, adds a link of outlier rejection after UKF filtering on detection data, and predicts the track of the target unmanned aerial vehicle at the same time; and then, the real-time feedback correction of the detection system to the target position is combined to adjust the deception signal in real time. The invention solves the problems of single sensor filtering, tracking and diverging, has the characteristics of high position precision, small jump range and the like, and improves the signal precision in the generation process of the deceptive jamming signal, thereby improving the final deceptive precision and the deception success rate of the unmanned plane.
Description
Technical Field
The invention belongs to the field of satellite navigation countermeasure, and relates to an unmanned aerial vehicle navigation spoofing method.
Background
Along with the rapid development of the unmanned aerial vehicle industry, the dangerous probability of uncontrollable flight of a large number of small unmanned aerial vehicles such as remote control helicopters, multi-rotor aircraft and the like which are easy to control in the field of forbidden flight of airports and the like and in activities such as large conferences, leaders visit, major sports events and the like is greatly increased, and the unmanned aerial vehicle black flight event forms a serious threat to security and protection work of social security management and even important place areas, so that the deception technology related to unmanned aerial vehicle satellite navigation starts to be widely focused.
The unmanned aerial vehicle satellite navigation deception technology achieves the purpose of disturbing target coordinates and time information mainly by transmitting deception satellite signals with the same structural information as a real navigation satellite. In the process of transmitting the deception signal, the received signal needs to be forwarded to the position needing deception according to the information of the actual position, speed and the like of the target unmanned aerial vehicle so as to achieve effective deception on the navigation equipment.
At present, aiming at accurate navigation spoofing of an unmanned aerial vehicle, detection information of detection equipment such as a radar is mainly used as reference position input of spoofing interference, and the spoofing coordinates of the unmanned aerial vehicle are set on the basis of the position. Because the existing detection equipment such as the radar has the problems of low precision and the like, the problem that the fraudulent signal jump is serious in the unmanned aerial vehicle spoofing process is easily caused, the stability is not high, the spoofing precision is poor, and the effect is poor. Meanwhile, in the existing unmanned aerial vehicle spoofing method, the detection system has a wild value phenomenon in the dynamic moving target positioning, single detection equipment is adopted as target position information input, the problem of divergence easily occurs during filtering tracking, and the accuracy of unmanned aerial vehicle navigation spoofing is further affected. On the other hand, in the existing unmanned aerial vehicle deception method, direct deception based on unmanned aerial vehicle detection position is generally easy to be perceived by operators or an automatic navigation system because of the problems of shake, large span and the like of deception tracks, and deception failure is caused.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides an unmanned aerial vehicle spoofing method based on UKF multi-source fusion detection, which adopts a radar/photoelectric composite guidance information fusion system to fusion detect targets, adds a link of outlier rejection after UKF filtering is carried out on detected data, reduces the problem of low spoofing precision caused by spoofing signal jump in the unmanned aerial vehicle spoofing process, and predicts the track of the target unmanned aerial vehicle. And then, the real-time feedback correction of the detection system to the target position is combined to adjust the deception signal in real time, so that the decoy precision is further improved.
The technical scheme adopted by the invention for solving the technical problems comprises the following steps:
Step S1: three-axis component of position and speed of target moving in three-dimensional space is used as state variable Establishing a state equation;
Step S2: interpolation processing is carried out on the photoelectric output data of the radar to obtain detection data with high updating rate, and meanwhile, an observation equation is established by taking the distance, azimuth angle and pitch angle Z R＝[rR,θR,φR]T of a radar target and the azimuth angle and pitch angle Z I＝[θI,φI]T of the photoelectric target as observation variables;
Step S3: carrying out nonlinear transformation on the state equation and the observation equation established in the step S1 and the step S2 by utilizing UT transformation, and establishing a UKF filter;
Step S4: removing outlier noise by adopting a new information discrimination method, and performing outlier discrimination on the measured data to reduce the jump situation of the deception signal;
Step S5: obtaining position and speed information of the target unmanned aerial vehicle according to the UKF filtered and updated state variable X, and predicting information of the next moment k+1;
Step S6: according to predicted position coordinates of unmanned aerial vehicle Calculating a pseudo range rho i between the ith satellite and the target unmanned aerial vehicle;
step S7: setting a decoy coordinate point (x p,yp,zp) according to the final position coordinate (x final,yfinal,zfinal) of the expected unmanned aerial vehicle to be decoy and the target unmanned aerial vehicle position coordinate, and calculating a pseudo-range change quantity delta rho i;
Step S8: generating a deception jamming signal according to the obtained pseudo-range change amount, and radiating the deception jamming signal to the target unmanned aerial vehicle;
Step S9: the composite detection system feeds back the track route of the unmanned aerial vehicle in real time, adjusts the offset coefficient according to the preset deception track and the real track of the unmanned aerial vehicle, and simultaneously returns to the step S5 to predict deception next step, so that deception interference on the target unmanned aerial vehicle is completed.
As a further improvement of the invention: in the step S1, the state equation:
X(k+1)＝F(k)X(k)+G(k)V(k) (1)
Wherein F (k) is a state transition matrix at the current k moment, G (k) is a noise distribution matrix, and V (k) is a noise vector.
Wherein T is the observation period.
As a further improvement of the invention: in said step S2, the observation equation:
Wherein h R(k)、hI (k) is an observation matrix of radar and photoelectricity, and W R(k)、WI (k) is a measurement noise matrix.
As a further improvement of the invention: in the step S3, the state estimation value of the radar obtained by using the UKF and the corresponding covariance matrix are respectively recorded asP R (k|k), the state estimation of the photoelectricity and the corresponding covariance matrix are respectively recorded as/>P I (k|k), fusion state estimation/>The covariance is P (k|k). The radar and photoelectric track weighted fusion method can be expressed as:
PIR(k|k)＝[PRI(k|k)T] (8)
Wherein the superscript R represents radar, I represents photoelectricity, and P RI and P IR are cross covariance matrices caused by common process noise, respectively.
As a further improvement of the invention: in the step S4, the outlier noise is removed by using the innovation discrimination method, and the norm of the innovation is determined by comparing the observed value with the measurement prediction:
A threshold L is set for Δz k, and when Δz k exceeds the threshold L, then the measurement z k is considered to be an outlier and the state prediction X (k|k-1) is used instead of z k.
Wherein the method comprises the steps of
Namely, taking the multiple of the increment of the target distance in the time interval of two sampling as a discrimination threshold, wherein the empirical value of the coefficient mu is between 4 and 6.
As a further improvement of the invention: in said step S6, according to the predicted position coordinates of the unmanned aerial vehicleCalculating a pseudo range rho i between the ith satellite and the target unmanned aerial vehicle, namely:
Wherein, (x si,ysi,zsi) is the position coordinate of the ith satellite; t u is the clock difference of the receiver; c is the speed of light.
As a further improvement of the invention: in the step S7, a decoy coordinate point (x p,yp,zp) is set according to the final position coordinate (x final,yfinal,zfinal) of the unmanned aerial vehicle which is expected to be decoy and the position coordinate of the target unmanned aerial vehicle, and a pseudo-range change amount Δρi is calculated, namely:
as a further improvement of the invention: in said step S9, the offset coefficient epsilon is derived from a preset fraud trajectory and a real trajectory of the drone,
Wherein x k、xk-1 represents the actual position of the target unmanned aerial vehicle at the time k and the time k-1, and x S represents the position of a preset decoy point at the time k; and then adjusting an offset coefficient according to a preset deception track and a real track of the unmanned aerial vehicle, if the real track is far away from the final deception position than the deception track, increasing epsilon, otherwise, decreasing epsilon.
The beneficial effects of the invention are as follows:
1) Compared with the traditional navigation spoofing target position acquisition mode, the radar/photoelectric composite guidance information fusion system is adopted as the navigation spoofing target position input, the problem of filtering, tracking and diverging of a single sensor is solved while the wild value of the dynamic moving target measurement information is removed, and the method has the characteristics of high position precision, small jump range and the like, and in the spoofing interference signal generation process, the signal precision is improved, and the final spoofing precision can be improved;
2) Compared with the traditional deception jamming method, the method aims at the problem that deception track setting is unreasonable and easy to find, track prediction is carried out on the target unmanned aerial vehicle, parameters such as deception signals, deception deflection angles and the like are adjusted in real time by combining the detection system, real-time feedback correction of the whole deception track is completed, and the deception precision and success rate of the unmanned aerial vehicle are improved.
Drawings
Fig. 1 is a schematic view of a fraud trajectory of the present invention.
Fig. 2 is a schematic diagram of the offset of the present invention.
Fig. 3 is a flow chart of the method of the present invention.
Detailed Description
The invention will be further illustrated with reference to the following figures and examples, which include but are not limited to the following examples.
Step S1: with three-axis components of position and velocity of the target as state variablesAnd establishing a state equation.
X(k+1)＝F(k)X(k)+G(k)V(k) (16)
Wherein F (k) is a state transition matrix at the current k moment, and G (k) is a noise distribution matrix.
V (k) = [ V x,vy,vz]T ] is independent zero-mean gaussian noise vector with variance ofAndCovariance matrix of state noise is
In the method, in the process of the invention,T is the observation period.
Step S2: interpolation processing is carried out on the photoelectric output data of the radar to obtain detection data with high updating rate, and meanwhile, an observation equation is established by taking the distance, azimuth angle and pitch angle Z R＝[rR,θR,φR]T of a radar target and the azimuth angle and pitch angle Z I＝[θI,φI]T of the photoelectric target as observation variables.
Wherein h R(k)、hI (k) is the observation matrix of the radar and the photoelectricity respectively.
W R(k)、WI (k) is zero-mean Gaussian white noise of the observation matrix, and the variance matrix is
In the method, in the process of the invention,The mean square error of the measured noise of radar distance, azimuth angle and pitching angle respectively; /(I) The mean square error of the measured noise of the azimuth angle and the pitch angle of the photoelectric respectively.
Step S3: and carrying out nonlinear transformation on the system model by utilizing UT transformation, and establishing a UKF filter.
Assuming that the mean and variance of the n-dimensional random vector X are respectivelyP 0, 2n+1 σ samples are performed on the state vector, and the corresponding weights ω i are calculated:
Wherein, The ith column, which is the square root of the matrix, can be obtained by the lower triangle Cholesky decomposition; λ=α 2 (n+κ) -n is a scale parameter, the parameter α determines the distribution of sampling points around the state mean, usually a very small positive number, and 10 -4 +.ltoreq.1 is desirable; the parameter k is typically set to 0 for state estimation; in the present invention, n=6.
Substituting sigma sampling points of the state vector predicted values into observation equations of the system, and calculating sigma sampling points of observed quantities
Calculating the mean and variance of observables by means of mean weighting and variance weighting
Updating state vector predictors and state vector variance matrix predictors
The state estimation values of the radar and the photoelectricity obtained through the calculation and the corresponding covariance matrix are respectively recorded as follows: p R(k|k)、PI (k|k), fusion state estimation/> The covariance is P (k|k). The radar and photoelectric track weighted fusion method can be expressed as:
PIR(k|k)＝[PRI(k|k)T] (32)
Wherein the superscript R represents radar, I represents photoelectricity, and P RI and P IR are cross covariance matrices caused by common process noise, respectively.
Step S4: and removing outlier noise by adopting a new information discrimination method, performing outlier identification on the measured data, and reducing the jump condition of the deception signal.
Comparing the observed value with the measurement prediction to judge the norm of the innovation:
A threshold L is set for Δz k, and when Δz k exceeds the threshold L, then the measurement z k is considered to be an outlier and the state prediction X (k|k-1) is used instead of z k.
Wherein the method comprises the steps of
Namely, taking the multiple of the increment of the target distance in the time interval of two sampling as a discrimination threshold, wherein the empirical value of the coefficient mu is between 4 and 6.
The state vector predicted value of the UKF filter equation in step S3 is changed to
In the method, in the process of the invention,And the corrected innovation value is the outlier.
Step S5: and obtaining the position and speed information of the target unmanned aerial vehicle according to the UKF filtered and updated state variable X, and predicting the information of the next moment k+1.
Step S6: according to predicted position coordinates of unmanned aerial vehicleCalculating a pseudo range rho i between the ith satellite and the target unmanned aerial vehicle;
Wherein, (x si,ysi,zsi) is the position coordinate of the ith satellite; t u is the clock difference of the receiver; c is the speed of light.
Step S7: setting a decoy coordinate point (x p,yp,zp) according to the final position coordinate (x final,yfinal,zfinal) of the expected unmanned aerial vehicle to be decoy and the target unmanned aerial vehicle position coordinate, and calculating a pseudo-range change quantity delta rho i;
Step S8: and generating a deception jamming signal according to the obtained pseudo-range quantity, and radiating the deception jamming signal to the target unmanned aerial vehicle.
Step S9: and the composite detection system feeds back the track route of the unmanned aerial vehicle in real time, adjusts the offset coefficient according to the predicted track and the real track of the unmanned aerial vehicle, and simultaneously returns to the step S5 to predict cheating in the next step, and finally, the cheating interference on the target unmanned aerial vehicle is completed.
After the predicted track of the unmanned aerial vehicle is obtained by the composite detection system, the unmanned aerial vehicle is deceptively set according to the predicted point and the preset deception point coordinates. After the deception is completed, the detection system is utilized to observe and feed back the actual position of the target unmanned aerial vehicle after deception. At this time, the deception position at the time k+1 can be estimated by adopting a similarity principle according to the relationship among the deception point, the predicted point and the actual position of the target unmanned aerial vehicle at the current time k. And (3) deflecting the next step of decoy track by adjusting the offset coefficient epsilon between the preset deception point and the actual position in each step, thereby completing the real-time feedback correction of the whole deception track. Meanwhile, in order to avoid the influence of the overlarge offset coefficient on the flight track, a threshold value can be set for the deflection angle, and the threshold value is less than or equal to 10 degrees.
The schematic diagram of the spoofing track is shown in fig. 1, in which a dotted line is a predicted track of the composite detection system on the unmanned aerial vehicle, a solid line is a real track of the unmanned aerial vehicle after being spoofed, and a dotted line is a preset spoofing track adjusted in real time according to the real track.
The offset coefficient epsilon can be obtained from a preset spoofing trajectory and a real trajectory of the drone, as shown in figure 2. In the figure, x k、xk-1 represents the actual position of the target unmanned aerial vehicle at the time k and the time k-1, x S represents the preset decoy point position at the time k, and the offset coefficient may be set as follows:
And then adjusting an offset coefficient according to a preset deception track and a real track of the unmanned aerial vehicle, if the real track is far away from the final deception position than the deception track, increasing epsilon, otherwise, decreasing epsilon.
Claims (9)
1. The unmanned aerial vehicle spoofing method based on UKF multisource fusion detection is characterized by comprising the following steps of:
Step S1: three-axis component of position and speed of target moving in three-dimensional space is used as state variable Establishing a state equation;
Step S2: interpolation processing is carried out on the photoelectric output data of the radar to obtain detection data with high updating rate, and meanwhile, an observation equation is established by taking the distance, azimuth angle and pitch angle Z R＝[rR,θR,φR]T of a radar target and the azimuth angle and pitch angle Z I＝[θI,φI]T of the photoelectric target as observation variables;
Step S3: carrying out nonlinear transformation on the state equation and the observation equation established in the step S1 and the step S2 by utilizing UT transformation, and establishing a UKF filter;
Step S4: removing outlier noise by adopting a new information discrimination method, and performing outlier discrimination on the measured data to reduce the jump situation of the deception signal;
Step S5: obtaining position and speed information of the target unmanned aerial vehicle according to the UKF filtered and updated state variable X, and predicting information of the next moment k+1;
Step S6: according to predicted position coordinates of unmanned aerial vehicle Calculating a pseudo range rho i between the ith satellite and the target unmanned aerial vehicle;
step S7: setting a decoy coordinate point (x p,yp,zp) according to the final position coordinate (x final,yfinal,zfinal) of the expected unmanned aerial vehicle to be decoy and the target unmanned aerial vehicle position coordinate, and calculating a pseudo-range change quantity delta rho i;
Step S8: generating a deception jamming signal according to the obtained pseudo-range change amount, and radiating the deception jamming signal to the target unmanned aerial vehicle;
Step S9: the composite detection system feeds back the track route of the unmanned aerial vehicle in real time, adjusts the offset coefficient according to the preset deception track and the real track of the unmanned aerial vehicle, and simultaneously returns to the step S5 to predict deception next step, so that deception interference on the target unmanned aerial vehicle is completed.
2. The unmanned aerial vehicle spoofing method based on kf multisource fusion detection according to claim 1, wherein in the step S1, the state equation X (k+1) =f (k) X (k) +g (k) V (k), where F (k) is the state transition matrix at the current k moment,G (k) is a noise distribution matrix,/>V (k) is a noise vector, and T is an observation period.
3. The unmanned aerial vehicle spoofing method based on kf multisource fusion detection according to claim 1, wherein in the step S2, the equation of observation isWherein h R(k)、hI (k) is the observation matrix of the radar and the photoelectricity, W R(k)、WI (k) is the measurement noise matrix,
4. The unmanned aerial vehicle spoofing method based on the uff multisource fusion detection according to claim 1, wherein in the step S3, the state estimation value of the radar obtained by using the kf and the corresponding covariance matrix are respectively recorded asP R (k|k), the state estimation of the photoelectricity and the corresponding covariance matrix are respectively recorded as/>P I (k|k), fusion state estimation/>The covariance is P (k|k), and the weighted fusion method of the radar and the photoelectric track is expressed as
PIR(k|k)＝[PRI(k|k)T]
Wherein the superscript R represents radar, I represents photoelectricity, and P RI and P IR are cross covariance matrices caused by common process noise, respectively.
5. The unmanned aerial vehicle spoofing method based on kf multisource fusion detection according to claim 1, wherein in the step S4, outlier noise is removed by adopting an innovation discrimination method, and the norm of the innovation is determined by comparing the observed value with the measurement predictionA threshold L is set for Δz k, and when Δz k exceeds the threshold L, then the measurement z k is considered to be an outlier and the state prediction X (k|k-1) is used instead of z k.
6. The unmanned aerial vehicle spoofing method based on the UKF multisource fusion detection according to claim 5, wherein a multiple of the increment of the target distance in the two sampling time intervals is used as a discrimination thresholdThe empirical value of the coefficient mu is 4 to 6.
7. The unmanned aerial vehicle spoofing method based on kf multisource fusion detection according to claim 1, wherein in step S6, based on predicted unmanned aerial vehicle position coordinatesCalculating pseudo range/>, between ith satellite and target unmanned aerial vehicleWherein, (x si,ysi,zsi) is the position coordinate of the ith satellite; t u is the clock difference of the receiver; c is the speed of light.
8. The unmanned aerial vehicle spoofing method based on the kf multisource fusion detection according to claim 1, wherein in the step S7, a spoofing coordinate point (x p,yp,zp) is set according to the final position coordinate (x final,yfinal,zfinal) in which the unmanned aerial vehicle is expected to be spoofed and the target unmanned aerial vehicle position coordinate, and the pseudo-range change amount Δρi is calculated.
9. The unmanned aerial vehicle spoofing method based on kf multisource fusion detection according to claim 1, wherein in step S9, the offset coefficient epsilon is derived from a preset spoofing trajectory and an unmanned aerial vehicle true trajectory,Wherein x k、xk-1 represents the actual position of the target unmanned aerial vehicle at the time k and the time k-1, and x S represents the position of a preset decoy point at the time k; and then adjusting an offset coefficient according to a preset deception track and a real track of the unmanned aerial vehicle, if the real track is far away from the final deception position than the deception track, increasing epsilon, otherwise, decreasing epsilon.
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